Electronic health records PICOT Evidence Table Worksheet
Abstract
Evidence synthesis is a crucial part of gathering background literature to support a proposal. Medication errors continue to be a problem plaguing healthcare institutions globally, the implementation of electronic health records has been proven to be an alleviating solution for this challenge. Five studies were evaluated in this proposal; the evidence synthesized supported the presented PICOT. The literature provided by the five studies in the synthesis evaluation table proved that electronic management of medications significantly reduces medication errors, and especially prescribing errors. Electronic health records PICOT Evidence Table Worksheet.
Evidence Table Worksheet
PICOT Question
In patients admitted to a psychiatric institution that converted from paper charting to electronic health records (P), does the implementation of electronic health records (I) compared to paper charting (C) decrease medication errors (O) within 6 months of its implementation (T). Electronic health records PICOT Evidence Table Worksheet
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- Will you have a comparison group or will subjects be their own controls?
The subjects will be their own control; the same group will be evaluated. The institution’s medication error event rate will be compared before electronic health records implementation and 6 months after its implementation
- Is a ‘time’ appropriate with your question—why or why not? Yes, the time is ideal for my PICOT. The institution under study is plagued with a high rate of medication safety events; it is possible to collect sufficient data within 6 months to evaluate the difference electronic health records made on the rate of medication errors Electronic health records PICOT Evidence Table Worksheet.
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I. Evidence Synthesis
| (Database) ex: Cochran | Study #1
Al-Sarawi, Polasek,Caughey,and Shakib (2019) |
Study #2
(Vaidotas, Yokota, Negrini, et al., 2019). |
Study #3
Loguidice, (2014) |
Study #4
(Hodgkinson, Larmour,Lin,et al., 2017) |
Study #5
(Priya,Thottumkal, Warrier, et al., 2017) |
Synthesis |
| (p) Population | 3 South Australian public hospitals | 4 Emergency departments (ED) in Brazil- A total of 327,017 patients were seen during this study | 79 residents in a long term care facility in the United States | 379 patients in an outpatient clinic in Australia | A quaternary care hospital in India | Multiple sample sizes and institution were evaluated as it relates to electronic health records and medication errors. |
| (i) Intervention | Implementation of an electronic prescribing system (e-prescribing) to reduce the rate of medication errors
|
Comparative analysis of medication error rates in the 4 ED: 2 ED had an electronic health records system implemented and the other 2 still used paper charting | Implementation of an integrated electronic health records system and an 8 hour orientation on the correct use of the electronic health records system | Implementation of an integrated electronic medication prescribing and dispensing system
Electronic health records PICOT Evidence Table Worksheet |
Prescriptions were audited using an electronic prescription auditing tool. | The studies primarily used comparative descriptive analysis to prove the effectiveness of various electronic health records systems in reducing medication error rates. The most compelling evidence was portrayed by Priya et al (2017) that proved the electronic auditing tool prevented an astonishing 140 medication errors out of a possible 226 interventions |
| (c) Comparison | Comparing the rate of medication errors in the 3 hospitals before and after the implementation of the e-prescribing system
|
The rate of medication errors were compared between the 2 emergency departments with an electronic health records system implemented and the other 2 without an electronic health records system implemented | Comparing the rate of medication discrepancies per resident before and after electronic health records implementation | Before and after study comparing the rate of medication errors before and after the electronic medication system implementation | 226 interventions were compared in a before and after audit for medication errors. | The studies compared a pre and post implementation status of medication error rates before and after an electronic medication system implementation |
| (o) Outcome | The e-prescribing system decreased medication errors from 67.7 per 100 orders to 2.8 per 100 orders
|
The 2 emergency departments that had an electronic health records system implemented had less medication errors than the emergency departments without electronic health records system implemented at a rate of 88 per million opportunities vs 164 per million opportunities. | Medication errors decreased from 9.2 per resident before implementation to 2.9 per resident after implementation | Implementation of an electronic integrated medication system reduced medication error rates by 93% | Of the 226 prescriptions that were audited, the electronic prescription auditing tool prevented 140 medication errors | Electronic systems decreased the rate of medication errors in all five studies |
| (t) time | Within two (2) years (2012-2014)
|
One (1) year | Within two (2) months | One (1) year | One (1) year | The time frame for all studies ranged from two (2) months to two (2) years |
- Evaluation Table
| Citation | Design | Sample size: Adequate? | Major Variables:
Independent Dependent |
Study findings: Strengths and weaknesses | Level of evidence | Evidence Synthesis |
|
Al-Sarawi, Polasek,Caughey,and Shakib (2019) |
A prospective structured medication chart audit before and after electronic prescribing system implementation.
|
3 South Australian public hospitals were audited; adequate. The sample size consisted of a small peripheral community hospital, a general metropolitan hospital and a general rural hospital. This sample size captures data from various perspectives. | The implementation of an electronic prescribing system (independent variable) in correlation with medication errors (dependent variable). | Strengths
-Demonstrates a positive connection between electronic prescribing systems in the reduction of medication errors. -Used differing sites (rural, community and metropolitan) to capture diverse data -Evidence obtained from at least 1 well-designed large multi-site
Weaknesses -Demographic locations limited to Australia. |
II | Electronic management of medications significantly reduces medication errors, and especially prescribing errors. |
| (Vaidotas, Yokota, Negrini, et al., 2019). | A cross- sectional, retrospective, descriptive, comparative study of medication errors
|
A total of 327,017 patients were seen in the 4 emergency departments under study | Medication errors were lower in the emergency departments that had electronic health records implementation as opposed the other two that were still paper charting. | Strengths
Evidence obtained from a well-designed large multi-site. Weaknesses -Findings cannot be generalized due to one study conducted in one geographical location -Failure to report the specific sample population used: elderly, adults or children since medication dosages in children, adults and elderly have different dosing |
II | The findings of this study support the use of EMRs (Electronic Medical Records) in EDs
to reduce the rates of medication errors. There were 88 events per million opportunities in the departments with electronic medical record and 164 events per million opportunities in the units with paper charting. Medication errors are a threat to patient safety and contribute towards adverse events, drug reactions and frequent visits to the ED |
| Loguidice, (2014) | Comparative study
|
79 residents in a long term care facility in the United States. Sample size adequate for gathering information on that population for that particular study. | The total and average number of medication discrepancies after electronic health records implementation | Strengths
-Proves that electronic health records plays a significant role in the reduction of medication errors
Weaknesses -Findings cannot be generalized due to one study conducted in one geographical location – 8 hour orientation on correct use of EHR is limited |
IV | The findings of this study support the use of EHRs (Electronic Health Records) in long term care facilities
to reduce the rates of medication errors |
| (Hodgkinson, Larmour,Lin,et al., 2017). | Before and after intervention study
|
379 patients in an outpatient clinic in Australia. Sample size adequate for the information the authors wanted to retrieve from that population. | Implementation of an integrated electronic medication prescribing and dispensing system (independent variable), decreased the rate of medication errors (dependent variable). | Strengths
-Proves that electronic prescribing system plays a significant role in the reduction of medication errors Weaknesses -Findings cannot be generalized due to one study conducted in one geographical location |
IV | The study concluded that the implementation of an electronic prescribing system significantly decreased the rate of prescribing errors in the population of study |
| (Priya, Thottumkal,Warrier,Krishna & Joseph, 2017) | Cross sectional comparative study.
|
1 quaternary care hospital in India. Sample size adequate. This is the largest hospital in that geographical location with a bed count of 370 | Implementation of an integrated electronic medication auditing tool (independent variable), decreased the rate of medication errors (dependent variable). | Strengths
-Proves that electronic prescription auditing tool reduces the rate of medication errors Weaknesses -Findings cannot be generalized due to one study conducted in one geographical location |
IV
Electronic health records PICOT Evidence Table Worksheet |
The study supported the notion that an electronic auditing tool decrease the number of medication errors |
Week Four Worksheet
PICOT
In patients admitted to a psychiatric institution that converted from paper charting to electronic health records (P) , does the implementation of electronic health records (I) compared to paper charting ( C) decrease medication errors (O) within 6 months of its implementation (T). Electronic health records PICOT Evidence Table Worksheet
| Research Tool | Search Tips | Search Terms & Limits | Findings | Features |
| CINAHL
CINAHL is an SU subscription-only resource that offers full text access to 336 scholarly journals and indexes over 3,000 journals from the fields of nursing and allied health. Indexed journals do not provide access to full-text. Just because it is indexed in does not mean the library has full-text access to the journal. SU can always request articles for students via Interlibrary Loan, but the service is not instantaneous |
§ Look at the Major Subject Heading in the Full Record
§ Use the Limits Feature: o Example: Publication Type=Systematic Review § Try the CINAHL Heading search:
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Keyword search: Electronic health records AND Medication errors
Limits: Full texts 2015-2020
CINAHL Heading search: Electronic health records AND medication errors Limits: Publication= systematic review Full texts 2015-2020 |
377
5
5 |
· Simple
· Easy to navigate · Modifiable search criteria |
| PubMed
PubMed is a free health science citation & abstracts index from the National Center for Biotechnology Information at the U.S. National Library of Medicine.
|
§ Look at MeSH Terms in Full Record
§ Use the Limits Feature: o Examples: Article Type=Meta-Analysis; Age=All Adult § Look for the open access Free articles!
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Keyword search: Electronic health records and medication errors
Limits: full-text; 5 years
MESH search: Limits: full-text; 5 years
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2
2 Electronic health records PICOT Evidence Table Worksheet |
· Difficulty understanding MeSH terms
· Limited results found for my topic · Best feature was sorting by ‘best match’ or ‘similar articles’
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| Cochrane Library
Cochrane Library provides access to the Cochrane Library of Systematic Reviews. Full text of reviews are subscription only. Index summaries are a public resource. Indexed journals do not provide access to full-text. Just because it is indexed in does not mean the library has full-text access to the journal. SU can always request articles for students via Interlibrary Loan, but the service is not instantaneous |
§ Use the Simple Search and the Advance Search Features
§ Allows you to search with MeSH Terms § Check out the New Reviews Browse reviews by topic |
Keyword search: Electronic health records and medication errors
Limits:2015-2020
MESH search: Limits:2015-2020
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0
0
0 |
· No result found
· Limited MeSH options
· Harder to navigate for additional search options
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| Dynamed
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· Use the Simple Search and the Advance Search Features
· Allows you to search with MeSH Terms · Check out the New Reviews Browse reviews by topic |
Keyword search: electronic health records and medication errors
Also browse by: electronic health records |
Electronic health records PICOT Evidence Table Worksheet
56 |
· No result generated for electronic health records and medication errors
· I was able to get general information on electronic health records (very minimal at that). 56 search results generated, but not specific to medication errors.
· MeSH search in this database was difficult to navigate.
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| TRIP Database
TRIP is a clinical search engine to locate publicly available clinical evidence.
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§ Limit to:
§ Systematic Reviews § Guidelines-US
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Keyword search: Electronic health records and medication error
Limits: 2015-2020 |
2,096
137 PICO results |
· Easy to navigate
· Yielded multiple results · The PICO search option was an asset · The color coding associated with the type of research or article makes it visually appealing
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| SU Library Search | · Library Search is setup just like an EBSCO
· The Library even has a specialized PICOT search setup in Library Search—must go to Advance search to get to PICOT option |
Keyword search: electronic health records and medication errors
Limit: 2015-2020 |
3,643
5 PICO results found |
· My most commonly used database
· The PICOT search option was the best · Easy to use · Produced many result options |
Conclusion
Medication errors are among the leading causes of harm to patients. Sources of error include dose administration, monitoring response, history taking, prescribing errors, and medication management cycle. The literature provided in the five studies supported that prescribing errors are the most serious types of medication errors. The literatures concluded in their studies that management of medications significantly reduces medication errors, and especially prescribing errors Electronic health records PICOT Evidence Table Worksheet.
References
Al-Sarawi F, Polasek T, Caughey G & Shakib S. (2019). Prescribing errors and adverse drug reaction documentation before and after implementation of e-prescribing using the Enterprise Patient Administration System Fares. Journal of Pharmacy Practice and Research, 1(49), 27–32.
Hodgkinson, M., Larmour, I., Lin, S., Stormont, A., & Paul, E. (2017, April 1). The impact of an integrated electronic medication prescribing and dispensing system on prescribing and dispensing errors: a before and after study. Journal of Pharmacy Practice and Research, 47(10), 110-120. doi: 10.1002/jppr.1243
Loguidice, C. (2014, July 10). Using Electronic Health Records to Reduce Medication Errors in Long-Term Care. Annals of Long Term Care, 22(8), 22-29. doi:https://eds-b-ebscohost-com.su.idm.oclc.org/eds/detail/detail?vid=4&sid=29fde5e8-ba50-4a83-8076-77bd34d4d33d%40pdc-v-sessmgr05&bdata=JnNpdGU9ZWRzLWxpdmU%3d#AN=107816281&db=rzh
Priya, K., Thottumkal, A., Warrier, A., Krishna, S., & Joseph, N. (2017, October 5). Impact of electronic prescription audit process to reduce outpatient medication errors. Indian Journal of Pharmaceutical Sciences, 79(6), 1017-1021. doi:https://eds-b-ebscohost-com.su.idm.oclc.org/eds/pdfviewer/pdfviewer?vid=1&sid=29fde5e8-ba50-4a83-8076-77bd34d4d33d%40pdc-v-sessmgr05 Electronic health records PICOT Evidence Table Worksheet
Vaidotas, M., Yokota, P. K. O., Negrini, N. M. M., Leiderman, D. B. D., Souza, V. P. D., Santos, O. F. P. D., & Wolosker, N. (2019). Medication errors in emergency departments: is electronic medical record an effective barrier? Einstein (São Paulo), 17(4).